Impact of technology-based interventions for children and young people with type 1 diabetes on key diabetes self-management behaviours and prerequisites: A systematic review

Background The role of technology in the self-management of type 1 diabetes mellitus (T1DM) among children and young people is not well understood. Interventions should aim to improve key diabetes self-management behaviours (self-management of blood glucose, insulin administration, physical activity and dietary behaviours) and prerequisites (psychological outcomes and HbA1c) highlighted in the UK guidelines of the National Institute for Health and Care Excellence (NICE) for management of T1DM. The purpose was to identify evidence to assess the effectiveness of technological tools in promoting aspects of these guidelines amongst children and young people. Methods A systematic review of English language articles was conducted using the following databases: Web of Science, PubMed, Scopus, NUSearch, SAGE Journals, SpringerLink, Google Scholar, Science Direct, Sport Discus, Embase, Psychinfo and Cochrane Trials. Search terms included paediatric, type one diabetes, technology, intervention and various synonyms. Included studies examined interventions which supplemented usual care with a health care strategy primarily delivered through a technology-based medium (e.g. mobile phone, website, activity monitor) with the aim of engaging children and young people with T1DM directly in their diabetes healthcare. Studies did not need to include a comparator condition and could be randomised, non-randomised or cohort studies but not single-case studies. Results Of 30 included studies (21 RCTs), the majority measured self-monitoring of blood glucose monitoring (SMBG) frequency, clinical indicators of diabetes self-management (e.g. HbA1c) and/or psychological or cognitive outcomes. The most positive findings were associated with technology-based health interventions targeting SMBG as a behavioural outcome, with some benefits found for clinical and/or psychological diabetes self-management outcomes. Technological interventions were well accepted by children and young people. For the majority of included outcomes, clinical relevance was deemed to be little or none. Conclusions More research is required to assess which elements of interventions are most likely to produce beneficial behavioural outcomes. To produce clinically relevant outcomes, interventions may need to be delivered for at least 1 year and should consider targeting individuals with poorly managed diabetes. It is not possible to determine the impact of technology-based interventions on insulin administration, dietary habits and/or physical activity behaviour due to lack of evidence

A P-value model for theoretical power analysis and its applications in multiple testing procedures

Background: Power analysis is a critical aspect of the design of experiments to detect an effect of a given size. When multiple hypotheses are tested simultaneously, multiplicity adjustments to p-values should be taken into account in power analysis. There are a limited number of studies on power analysis in multiple testing procedures. For some methods, the theoretical analysis is difficult and extensive numerical simulations are often needed, while other methods oversimplify the information under the alternative hypothesis. To this end, this paper aims to develop a new statistical model for power analysis in multiple testing procedures. Methods: We propose a step-function-based p-value model under the alternative hypothesis, which is simple enough to perform power analysis without simulations, but not too simple to lose the information from the alternative hypothesis. The first step is to transform distributions of different test statistics (e.g., t, chi-square or F) to distributions of corresponding p-values. We then use a step function to approximate each of the p-value’s distributions by matching the mean and variance. Lastly, the step-function-based p-value model can be used for theoretical power analysis. Results: The proposed model is applied to problems in multiple testing procedures. We first show how the most powerful critical constants can be chosen using the step-function-based p-value model. Our model is then applied to the field of multiple testing procedures to explain the assumption of monotonicity of the critical constants. Lastly, we apply our model to a behavioral weight loss and maintenance study to select the optimal critical constants. Conclusions: The proposed model is easy to implement and preserves the information from the alternative hypothesis

Imaging biomarker roadmap for cancer studies.

Imaging biomarkers (IBs) are integral to the routine management of patients with cancer. IBs used daily in oncology include clinical TNM stage, objective response and left ventricular ejection fraction. Other CT, MRI, PET and ultrasonography biomarkers are used extensively in cancer research and drug development. New IBs need to be established either as useful tools for testing research hypotheses in clinical trials and research studies, or as clinical decision-making tools for use in healthcare, by crossing 'translational gaps' through validation and qualification. Important differences exist between IBs and biospecimen-derived biomarkers and, therefore, the development of IBs requires a tailored 'roadmap'. Recognizing this need, Cancer Research UK (CRUK) and the European Organisation for Research and Treatment of Cancer (EORTC) assembled experts to review, debate and summarize the challenges of IB validation and qualification. This consensus group has produced 14 key recommendations for accelerating the clinical translation of IBs, which highlight the role of parallel (rather than sequential) tracks of technical (assay) validation, biological/clinical validation and assessment of cost-effectiveness; the need for IB standardization and accreditation systems; the need to continually revisit IB precision; an alternative framework for biological/clinical validation of IBs; and the essential requirements for multicentre studies to qualify IBs for clinical use.Development of this roadmap received support from Cancer Research UK and the Engineering and Physical Sciences Research Council (grant references A/15267, A/16463, A/16464, A/16465, A/16466 and A/18097), the EORTC Cancer Research Fund, and the Innovative Medicines Initiative Joint Undertaking (grant agreement number 115151), resources of which are composed of financial contribution from the European Union's Seventh Framework Programme (FP7/2007-2013) and European Federation of Pharmaceutical Industries and Associations (EFPIA) companies' in kind contribution

Variation in Complexity of Infection and Transmission Stability between Neighbouring Populations of Plasmodium vivax in Southern Ethiopia

Spreadsheets and other data outputs that support a PLOS One paper investigating variation in complexity of infection and transmission Stability between neighbouring populations of Plasmodium vivax in Southern Ethiopi

Alterations in White Matter Microstructure and Connectivity in Young Adults with Alcohol Use Disorder.

Background: Magnetic resonance imaging (MRI) studies have shown differences in volume and structure in the brains of individuals with alcohol use disorder (AUD). Most research has focused on neuropathological effects of alcohol that appear after years of chronic alcohol misuse. However, few studies have investigated white matter (WM) microstructure and diffusion MRI-based (DWI) connectivity during early stages of AUD. Therefore, the goal of this work was to investigate WM integrity and structural connectivity in emerging adulthood AUD subjects using both conventional DWI metrics and a novel connectomics approach. Methods: Twenty-two AUD and eighteen controls (CON) underwent anatomical and diffusion MRI. Outcome measures were scalar diffusion metrics and structural network connectomes. Tract Based Spatial Statistics was used to investigate group differences in diffusion measures. Structural connectomes were used as input into a community structure procedure to obtain a co-classification index matrix (an indicator of community association strength) for each subject. Differences in co-classification and structural connectivity (indexed by streamline density) were assessed via the Network Based Statistics Toolbox. Results: AUD had higher FA values throughout the major WM tracts, but also had lower FA values in WM tracts in the cerebellum and right insula ($p_{\textrm{TFCE}}$ < 0.05). Mean diffusivity was generally lower in the AUD group ($p_{\textrm{TFCE}}$ < 0.05). AUD had lower co-classification of nodes between ventral attention and default mode networks, and higher co-classification between nodes of visual, default mode, and somatomotor networks. Additionally, AUD had higher fiber density between an adjacent pair of nodes within the default mode network. Conclusion: Our results indicate that emerging adulthood AUD subjects may have differential patterns of FA and distinct differences in structural connectomes compared to CON. These data suggest that such alterations in microstructure and structural connectivity may uniquely characterize early stages of AUD and/or a predisposition for development of AU

Small Sample Approach, and Statistical and Epidemiological Aspects

In this chapter, the design of pharmacokinetic studies and phase III trials in children is discussed. Classical approaches and relatively novel approaches, which may be more useful in the context of drug research in children, are discussed. The burden of repeated blood sampling in pediatric pharmacokinetic studies may be overcome by the population pharmacokinetics approach using nonlinear mixed effect modeling as the statistical solution to sparse data. Indications and contraindications for phase III trials are discussed: only when there is true "equipoise" in the medical scientific community, it is ethical to conduct a randomized clinical trial. The many reasons why a pediatric trial may fail are illustrated with examples. Inadequate sample sizes lead to inconclusive results. Twelve classical strategies to minimize sample sizes are discussed followed by an introduction to group sequential design, boundaries design, and adaptive design. The evidence that these designs reduce sample sized between 35 and 70% is reviewed. The advantages and disadvantages of the different approaches are highlighted to give the reader a broad idea of the design types that can be considered. Finally, working with DMCs during the conduct of trials is introduced. The evidence regarding DMC activities, interim analysis results, and early termination of pediatric trials is presented. So far reporting is incomplete and heterogeneous, and users of trial reports may be misled by the results. A proposal for a checklist for the reporting of DMC issues, interim analyses, and early stopping is presente